Bibliographic record
Abstract
International Journal of Chemistry wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal is greatly appreciated. Many authors, regardless of whether International Journal of Chemistry publishes their work, appreciate the helpful feedback provided by the reviewers.Reviewers for Volume 9, Number 3 Ahmad Galadima, Usmanu Danfodiyo University, NigeriaAhmet Ozan Gezerman, Yildiz Technical University, TurkeyAmer A. Taqa, Mosul University, IraqAsghari Gul, Comsats IIT, PakistanAyodele Temidayo Odularu, University of Fort Hare, South AfricaChanchal Kumar Malik, Vanderbilt University, USADiego Antonio Alonso, Alicante University, SpainFarkhondeh Fathi, University of Toronto, CanadaGreg Peters, University of Findlay, USAJiajue Chai, Brown University, USAJignasu P. Mehta, Bhavnagar University, IndiaJuan Rafael Garcia, Research Institute on Catalysis and Pertrochemistry (INCAPE), ArgentinaK. Ishara Silva, Rensselaer Polytechnic Institute, USAKallen Mulilo Nalyanya, Egerton University, KenyaLeiming Wang, Konica Minolta Laboratory, USAMeriem Belhachemi, University of Bechar, AlgeriaMonira Nessem Michael, National institute of standards (NIS), EgyptMustafa Oguzhan Kaya, Siirt University, TurkeyPriyanka Singh, University of Iowa, USAR. K. Dey, Birla Institute of Technology (BIT), IndiaRabia Rehman, University of the Punjab, PakistanSaroj Kumar Panda, Research & Development Center, Saudi ArabiaSujan Kumar Sarkar, Ruhr University Bochum, GermanyThirupathi Barla, Harvard University, USAVijay Ramalingam, Columbia University, USAWaseem Hassan, Universidade Federal de Santa Maria, Brazil Albert JohnOn behalf of,The Editorial Board of International Journal of ChemistryCanadian Center of Science and Education
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".